A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion

• URL: http://arxiv.org/abs/2503.16667v1
• Date: Thu, 20 Mar 2025 19:29:38 GMT
• Title: A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
• Authors: Oriol Vendrell-Gallart, Nima Negarandeh, Zahra Zanjani Foumani, Mahsa Amiri, Lorenzo Valdevit, Ramin Bostanabad,
• Abstract summary: A major challenge that impedes the construction of foundation process-property models is data scarcity.<n>We generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF)<n>We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs' interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications.

Related papers

• MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery [7.1240120153291535]
  We introduce MatterTune, a framework that provides advanced fine-tuning capabilities and seamless integration of atomistic foundation models into downstream materials informatics and simulation. MatterTune supports a number of state-of-the-art foundation models such as ORB, MatterSim, JMP, and EquformerV2.
  arXiv  Detail & Related papers  (2025-04-14T19:12:43Z)
• Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
  It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs) By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
  arXiv  Detail & Related papers  (2025-03-18T02:14:49Z)
• Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models [104.17057231661371]
  Time series analysis is crucial for understanding dynamics of complex systems.<n>Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs)<n>Their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints.<n>This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
  arXiv  Detail & Related papers  (2025-03-14T13:53:46Z)
• Foundation Model for Composite Materials and Microstructural Analysis [0.0]
  We present a foundation model specifically designed for composite materials.<n>Our findings validate the feasibility and effectiveness of foundation models in composite materials.<n>This framework enables high-accuracy predictions even when experimental data are scarce.
  arXiv  Detail & Related papers  (2024-11-10T19:06:25Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing [0.0]
  Metal additive manufacturing (MAM) has experienced significant developments and gained much attention. Predicting the impact of processing parameters on the characteristics of an MAM printed clad is challenging due to the complex nature of MAM processes. Machine learning (ML) techniques can help connect the physics underlying the process and processing parameters to the clad characteristics.
  arXiv  Detail & Related papers  (2023-07-04T18:32:41Z)
• Modular machine learning-based elastoplasticity: generalization in the context of limited data [0.0]
  We discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data.
  arXiv  Detail & Related papers  (2022-10-15T17:35:23Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery [1.0036312061637764]
  Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is scarcely populated and of dubious quality. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure-property relationships from the literature.
  arXiv  Detail & Related papers  (2021-11-02T21:43:58Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)
• Intelligent multiscale simulation based on process-guided composite database [0.0]
  We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
  arXiv  Detail & Related papers  (2020-03-20T20:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.